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name: Proteomics description: Proteomics analysis toolkit for label-free quantitative proteomics. Invokes R scripts for normalization, visualization (volcano, heatmap, PCA, LOPIT), pathway analysis (KEGG, ConsensusPathDB), and protein list cross-referencing (MISEV2018, SASP, Matrisome). USE WHEN user says 'analyze proteomics', 'volcano plot', 'normalize protein data', 'pathway enrichment', 'check EV markers', 'SASP analysis', 'matrisome', OR mentions q-value, fold-change, or protein quantification.

Proteomics

Quantitative proteomics analysis toolkit combining R script invocation with embedded methodology knowledge. Fully portable - all scripts and reference data included.

Skill Directory: ~/.claude/Skills/Proteomics/

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Workflow Routing

When executing a workflow, output this notification:

Running the **WorkflowName** workflow from the **Proteomics** skill...

Workflow	Trigger	File
Normalize	"normalize data", "apply normalization", "median/quantile/loess normalize"	workflows/Normalize.md
VolcanoPlot	"volcano plot", "create volcano", "visualize fold change"	workflows/VolcanoPlot.md
Heatmap	"heatmap", "PCA", "correlation plot", "sample clustering"	workflows/Heatmap.md
PathwayAnalysis	"pathway analysis", "KEGG enrichment", "ConsensusPathDB", "GO enrichment"	workflows/PathwayAnalysis.md
ProteinListQuery	"check EV markers", "MISEV proteins", "exosome markers", "blood contaminants"	workflows/ProteinListQuery.md
ExcelWorkup	"create Excel report", "filter by q-value", "generate data tables"	workflows/ExcelWorkup.md
Matrisome	"matrisome analysis", "ECM proteins", "extracellular matrix"	workflows/Matrisome.md
SaspAnalysis	"SASP analysis", "senescence factors", "core SASP"	workflows/SaspAnalysis.md

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Examples

Example 1: Generate Volcano Plot

User: "Create a volcano plot for my proteomics comparison data"
-> Invokes VolcanoPlot workflow
-> Asks for data file location and parameters (q-value, fold-change threshold)
-> Either invokes Plot_Workup_V10.R or generates custom ggplot2 code
-> Outputs TIFF files to output/ directory

Example 2: Check for EV Markers

User: "Which MISEV2018 EV markers are in my dataset?"
-> Invokes ProteinListQuery workflow
-> Reads user's protein list
-> Cross-references against data/MISEV2018_EV_Markers.txt
-> Returns categorized matches (Category 1-5, tetraspanins, annexins, etc.)

Example 3: Full Analysis Pipeline

User: "Run a complete proteomics analysis on my kidney data"
-> Sequences multiple workflows:
  1. Normalize (median normalization)
  2. Heatmap (PCA, sample correlation)
  3. VolcanoPlot (for each comparison)
  4. Matrisome (ECM protein analysis)
  5. SaspAnalysis (if relevant)
  6. ExcelWorkup (generate report)
-> Creates organized output/ directory structure

Example 4: Pathway Enrichment

User: "Run KEGG pathway analysis on my significantly altered proteins"
-> Invokes PathwayAnalysis workflow
-> Filters to q < 0.01, |log2FC| > 0.58
-> Runs clusterProfiler or ConsensusPathDB
-> Generates dotplot visualization

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R Script Quick Reference

All scripts are in the skill's rscripts/ directory.

Script	Purpose	Key Parameters
Plot_Workup_V10.R	Full visualization pipeline	organism, batch, myFC, myQval, mypattern
Excel_Workup_v05.R	Excel report generation	myoutput, batch, myFC, q-value flags
normalization/Step_1_Normalization.R	Data normalization	Input matrix (iMat)
ConsensusPathDB_23_0411_v03.R	Pathway dotplots	input_dir, output_dir, q.val, t.level
toolkit.R	Library loading	Called at start of analysis
barplots.R	Bar plot utility	Various

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Standard Parameters

Parameter	Typical Values	Description
q-value	0.05, 0.01, 0.001	Statistical significance threshold
Fold Change	0.58 (1.5x), 1.0 (2x)	Log2 fold change cutoff
Organism	"human", "mouse"	Species for reference lists
Pattern	"JB\\d_\\d+"	Regex for sample ID extraction

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Reference Data Available

All protein lists are in the skill's data/ directory.

List	File	Contents
MISEV2018 EV Markers	MISEV2018_EV_Markers.txt	500+ proteins, Category 1-5
EV Categories	MISEV2018_EV_Categories.txt	Category definitions
Exosome Markers	Exosome_Protein_Markers.txt	CD63, CD81, CD9, TSG101, etc.
Blood Contaminants	Top_10_Blood_Proteins.txt	Albumin, IgG, fibrinogen, etc.
Apolipoproteins	Apolipoproteins.txt	APOA1, APOB, etc.
Human Core SASP	Human_Core_SASP.csv	175 SASP factors with IR/RAS/ATV scores
Mouse Core SASP	Mouse_Core_SASP.csv	Mouse SASP orthologs
Human Matrisome	matrisome_hs_masterlist.csv	ECM proteins by category
Mouse Matrisome	matrisome_mm_masterlist.csv	Mouse ECM proteins

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Required Data Structure

For running the full analysis scripts, data should be organized as:

[PROJECT_DIR]/
├── data/
│   ├── [batch]_Protein_Report_2pep.csv    # Protein intensities
│   ├── [batch]_candidates_2pep.csv         # Comparison results
│   └── [batch]_ConditionSetup.csv          # Sample metadata
└── output/
    ├── Data_Tables/                        # Excel reports
    └── [plots will be saved here]

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Invocation Pattern

To run R scripts from this skill:

cd [PROJECT_WORKING_DIR]
Rscript ~/.claude/Skills/Proteomics/rscripts/[SCRIPT_NAME].R

Important: Scripts expect:

1. Working directory set to project folder
2. data/ subdirectory with input files
3. output/ subdirectory for results
4. Reference data paths point to skill's data/ directory (may need adjustment)

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When NOT to Use This Skill

• General R coding questions -> Use standard Claude
• Non-proteomics data analysis -> Use appropriate tools
• Genomics/transcriptomics -> Different methodology
• Statistical consulting without data -> Explain methodology, don't run